DeepHazard: neural network for time-varying risks

Abstract

Prognostic models in survival analysis are aimed at understanding the relationship between patients' covariates and the distribution of survival time. Traditionally, semi-parametric models, such as the Cox model, have been assumed. These often rely on strong proportionality assumptions of the hazard that might be violated in practice. Moreover, they do not often include covariate information updated over time. We propose a new flexible method for survival prediction: DeepHazard, a neural network for time-varying risks. Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time. A flexible implementation, allowing different optimization methods, along with any norm penalty, is developed. Numerical examples illustrate that our approach outperforms existing state-of-the-art methodology in terms of predictive capability evaluated through the C-index metric. The same is revealed on the popular real datasets as METABRIC, GBSG, and ACTG.

References in corpus (7)

• DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
• A Scalable Discrete-Time Survival Model for Neural Networks
• Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
• Deep Learning for Patient-Specific Kidney Graft Survival Analysis
• DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values
• Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
• Survival analysis as a classification problem

Cited by in corpus (2)

• Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
• Deep Neural Networks Guided Ensemble Learning for Point Estimation